Medical tests are commonly used as tools for the diagnosis of a number of pathologies—e.g., following the report of corresponding symptoms. For this purpose, different techniques are available in the art.
For example, the gold standard technique for cancer diagnosis (e.g., in prostate, liver, and breast) is biopsy, where samples of relevant tissues (commonly referred to as cores) are removed from a patient for examination. However, biopsy is a very invasive and expensive procedure. Moreover, biopsy is relatively inaccurate in specific applications (for example, its success rate is only approximately 70% in prostate cancer diagnosis, even with new strategies based on a higher number of cores).
Contrast-enhanced ultrasound analysis is another diagnostic technique that finds increasing applications in the same field. Generally, this diagnostic technique is based on the administration of an ultrasound contrast agent (UCA) to the patient—for example, a suspension of phospholipid-stabilized gas-filled microvesicles (or microbubbles); these contrast agent microbubbles act as efficient ultrasound reflectors, and can be easily detected by applying ultrasound waves and measuring the echo signals that are returned in response thereto. Since the contrast agent flows at the same velocity as the red-blood cells in the patient, its detection and tracking provides information about blood perfusion in a body-part under analysis (from which information about its condition can be derived).
Particularly, in an imaging approach, image sequences representing an evolution of the contrast agent in the body-part during the perfusion process are generated (where the values of each pixel in the images represent an intensity of the recorded echo signal over time for a corresponding location of the body-part). Therefore, examination of such image sequences (for example, displayed on a monitor) provides only a qualitative indication of the blood perfusion in the body-part.
Conversely, in a quantitative approach, the echo signals recorded during the whole perfusion process are fitted by mathematical model functions (for example, as disclosed in WO-A-2004/110279, the entire disclosure of which is incorporated herein by reference). The instances of the model functions so obtained can then be used to calculate different perfusion parameters (such as a wash-in rate, a wash-out rate, and the like). Any perfusion parameter may be calculated from a global echo signal that is obtained in a predefined Region Of Interest (ROI) comprising more than one pixel (with the perfusion parameter that is then presented as a single value). Alternatively, any perfusion parameter may be calculated from the echo signal of each pixel individually; a parametric image is then generated by graphically representing the value of the perfusion parameter for each corresponding pixel (preferably in a color-coded representation). The perfusion parameters provide a quantitative assessment of the blood perfusion in the body-part (with the parametric images representing a spatial map of the perfusion parameters throughout the body-part).
With reference in particular to prostate cancer diagnosis, studies using targeted biopsy under contrast-enhanced ultrasound guidance have shown an increase of its success rate (with the possibility of reducing the number of required cores). Moreover, contrast-enhanced ultrasound analysis could also replace biopsy as the first choice in the prostate cancer diagnosis (with a dramatic reduction of side-effects, costs, and patient morbidity).
For this purpose, the use of contrast-enhanced ultrasound analyses as a tool for the diagnosis of prostate cancer requires detection and characterization of corresponding lesions in the body-part. More specifically, the lesions are detected according to differences in perfusion kinetics compared to normal parenchymal tissue (i.e., earlier and faster wash-in and wash-out of the contrast agent). The lesions can then be characterized (in order to differentiate benign lesions from malignant lesions) according to differences in their vascular properties (i.e., density and/or structure of corresponding microvascular networks).
Parametric analyses may be used to detect the lesions. Indeed, the examination of parametric images based on corresponding perfusion parameters (such as the wash-in rate and the wash-out rate) allows detecting the lesions by localizing regions in the body-part with high values of these wash-in and wash-out rates. However, reliable parametric analyses generally require images that are spatially sub-sampled—i.e. pixel values of groups of neighboring pixels are low-pass filtered and then sub-sampled (according to a sub-sampling factor) to produce cell values for corresponding cells, on which the fitting operation is then performed. In this way, it is possible to increase a signal-to-noise ratio (SNR)—normally very low in the original echo signals—and to reduce a computation time—normally very high because of the complexity of the fitting operation and the large number of pixels. However, spatial sub-sampling generates parametric images with degraded resolution (which is not optimal for the characterization of the lesions). Moreover, echo signals must be recorded over an extended duration (encompassing the wash-in phase and a substantial part of the wash-out phase) in order to guarantee an acceptable robustness of the fitting operation (and then to provide reliable perfusion parameter estimates). Therefore, the echo signals are usually processed off-line (with a post-processing time that can easily exceed 3-8 minutes); proceeding in this way prevents any real-time examination of the body-part.
Imaging analyses may instead be used to characterize the lesions. Indeed, the examination of the images representing blood perfusion in the body-part (at full resolution) is useful to determine its vascular properties. However, identification of tiny blood vessels (such as capillaries) is challenging because the local contrast agent concentration can be very low (with blood vessels that may even contain only a single contrast agent microbubble as they are being imaged).
In order to solve this problem, a solution known in the art involves the application of a Maximum Intensity Projection (MIP) algorithm to the images (for example, as disclosed in U.S. Pat. No. 6,676,606, the entire disclosure of which is incorporated herein by reference). Particularly, for each pixel the maximum intensity projection algorithm holds the corresponding values in the different images to their maximum over time. In this way, trajectories of contrast agent particles are projected spatially, so as to emphasize the corresponding blood vessel morphology. However, in this way the images become diffuse as soon as the contrast agent starts perfusing the parenchymal tissue surrounding the lesions; therefore, the representation of the vascular properties of the lesions gets blurred and loses conspicuity (thereby considerably reducing the effectiveness of the imaging analyses for lesion characterization).
A Minimum Intensity Projection (mIP) algorithm is also known in the art; in this case, for each pixel the minimum intensity projection algorithm holds the corresponding values in the different images to their minimum over time. The minimum intensity projection algorithm may be used before contrast agent arrival in the images to suppress background clutter and improve the visualization of the contrast agent (for example, as suggested in U.S. Pat. No. 6,436,049, the entire disclosure of which is incorporated herein by reference); however, this algorithm is completely ineffective with respect of the above-mentioned problems.
It should be noted that the imaging analyses based on the maximum intensity projection algorithm might also be used to perform a qualitative detection of the lesions—e.g., in locations of the body-part that exhibit an early enhancement of the contrast agent during the wash-in phase. However, the maximum values of the echo signals for the lesions and the parenchymal tissue may be similar, so that their representations after application of the maximum intensity projection algorithm become similar at times after reaching their corresponding peaks; therefore, this approach is useful to emphasize differences in the perfusion kinetics only during the short period of the wash-in phase. In any case, any information about the wash-out phase is completely lost (since the pixel values remain constant after reaching their peaks).